关键词: 中文简历, 简历解析, 特征融合, 词向量, 神经网络

Abstract: It’s typical for the Chinese resume analysis to apply the rule-based and statistical-based methods, suffering from the low efficiency, high cost and poor generalization ability. This paper proposes a Chinese resume analysis method based on feature fusion model. The concatenation of the word vectors generated by Word2Vec and the word representation is generated from BLSTM neural network, then the text resume is analyzed by intergrating the BLSTM and CRF model（BLSTM-CRF）. In order to improve the efficiency of Chinese resume resolution, the two vectors are concatenated into a new word representation. Furthermore, the BLSTM layer is used to fuse the contextual information of the words to be marked, and then the values of all possible tag sequences are exported to the CRF layer. Finally, according to the constraints of the front and rear labels, the CRF is utilized to obtain the optimal labeling sequence. All of the neural networks are trained by the gradient descent algorithm and are optimized by the pretrained word embeddings and Dropout. The experimental results show that the feature fusion method is superior to the traditional resume analysis schemes.

Key words: Chinese resume, resume analysis, feature fusion, word vectors, neural network